Overview

Dataset statistics

Number of variables9
Number of observations630
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.4 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

sr is highly overall correlated with rr and 7 other fieldsHigh correlation
rr is highly overall correlated with sr and 7 other fieldsHigh correlation
t is highly overall correlated with sr and 7 other fieldsHigh correlation
lm is highly overall correlated with sr and 7 other fieldsHigh correlation
bo is highly overall correlated with sr and 7 other fieldsHigh correlation
rem is highly overall correlated with sr and 7 other fieldsHigh correlation
sr.1 is highly overall correlated with sr and 7 other fieldsHigh correlation
hr is highly overall correlated with sr and 7 other fieldsHigh correlation
sl is highly overall correlated with sr and 7 other fieldsHigh correlation
sl is uniformly distributedUniform
sr.1 has 127 (20.2%) zerosZeros

Reproduction

Analysis started2023-10-04 09:12:47.055438
Analysis finished2023-10-04 09:13:10.112164
Duration23.06 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

sr
Real number (ℝ)

Distinct627
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.6
Minimum45
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:10.316143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile46.258
Q152.5
median70
Q391.25
95-th percentile98.9936
Maximum100
Range55
Interquartile range (IQR)38.75

Descriptive statistics

Standard deviation19.372833
Coefficient of variation (CV)0.27057029
Kurtosis-1.5466136
Mean71.6
Median Absolute Deviation (MAD)19.02
Skewness0.11393461
Sum45108
Variance375.30666
MonotonicityNot monotonic
2023-10-04T14:43:10.784862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2
 
0.3%
80 2
 
0.3%
50 2
 
0.3%
93.8 1
 
0.2%
56 1
 
0.2%
81.8 1
 
0.2%
49.88 1
 
0.2%
52.16 1
 
0.2%
98.624 1
 
0.2%
98.4 1
 
0.2%
Other values (617) 617
97.9%
ValueCountFrequency (%)
45 1
0.2%
45.04 1
0.2%
45.08 1
0.2%
45.12 1
0.2%
45.16 1
0.2%
45.2 1
0.2%
45.24 1
0.2%
45.28 1
0.2%
45.32 1
0.2%
45.36 1
0.2%
ValueCountFrequency (%)
100 1
0.2%
99.968 1
0.2%
99.936 1
0.2%
99.904 1
0.2%
99.872 1
0.2%
99.84 1
0.2%
99.808 1
0.2%
99.776 1
0.2%
99.744 1
0.2%
99.712 1
0.2%

rr
Real number (ℝ)

Distinct626
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.8
Minimum16
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:11.300448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile16.5032
Q118.5
median21
Q325
95-th percentile28.9936
Maximum30
Range14
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.9661108
Coefficient of variation (CV)0.18193169
Kurtosis-0.95725379
Mean21.8
Median Absolute Deviation (MAD)3
Skewness0.45586255
Sum13734
Variance15.730035
MonotonicityNot monotonic
2023-10-04T14:43:11.800417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 2
 
0.3%
20 2
 
0.3%
26 2
 
0.3%
18 2
 
0.3%
17.408 1
 
0.2%
27.152 1
 
0.2%
22.48 1
 
0.2%
17.952 1
 
0.2%
18.432 1
 
0.2%
28.624 1
 
0.2%
Other values (616) 616
97.8%
ValueCountFrequency (%)
16 1
0.2%
16.016 1
0.2%
16.032 1
0.2%
16.048 1
0.2%
16.064 1
0.2%
16.08 1
0.2%
16.096 1
0.2%
16.112 1
0.2%
16.128 1
0.2%
16.144 1
0.2%
ValueCountFrequency (%)
30 1
0.2%
29.968 1
0.2%
29.936 1
0.2%
29.904 1
0.2%
29.872 1
0.2%
29.84 1
0.2%
29.808 1
0.2%
29.776 1
0.2%
29.744 1
0.2%
29.712 1
0.2%

t
Real number (ℝ)

Distinct626
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.8
Minimum85
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:12.316004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile86.258
Q190.5
median93
Q395.5
95-th percentile98.2452
Maximum99
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5296897
Coefficient of variation (CV)0.038035449
Kurtosis-0.67574733
Mean92.8
Median Absolute Deviation (MAD)2.504
Skewness-0.28575116
Sum58464
Variance12.458709
MonotonicityNot monotonic
2023-10-04T14:43:12.784723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 2
 
0.3%
90 2
 
0.3%
94 2
 
0.3%
92 2
 
0.3%
91.84 1
 
0.2%
95.744 1
 
0.2%
94.432 1
 
0.2%
88.28 1
 
0.2%
88 1
 
0.2%
97.704 1
 
0.2%
Other values (616) 616
97.8%
ValueCountFrequency (%)
85 1
0.2%
85.04 1
0.2%
85.08 1
0.2%
85.12 1
0.2%
85.16 1
0.2%
85.2 1
0.2%
85.24 1
0.2%
85.28 1
0.2%
85.32 1
0.2%
85.36 1
0.2%
ValueCountFrequency (%)
99 1
0.2%
98.976 1
0.2%
98.952 1
0.2%
98.928 1
0.2%
98.904 1
0.2%
98.88 1
0.2%
98.856 1
0.2%
98.832 1
0.2%
98.808 1
0.2%
98.784 1
0.2%

lm
Real number (ℝ)

Distinct626
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.7
Minimum4
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:13.237819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.0064
Q18.5
median11
Q315.75
95-th percentile18.4968
Maximum19
Range15
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation4.2996291
Coefficient of variation (CV)0.36748967
Kurtosis-1.1102046
Mean11.7
Median Absolute Deviation (MAD)3.212
Skewness0.16266708
Sum7371
Variance18.486811
MonotonicityNot monotonic
2023-10-04T14:43:13.722156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 2
 
0.3%
10 2
 
0.3%
17 2
 
0.3%
8 2
 
0.3%
6.816 1
 
0.2%
17.576 1
 
0.2%
12.6 1
 
0.2%
7.904 1
 
0.2%
8.432 1
 
0.2%
18.312 1
 
0.2%
Other values (616) 616
97.8%
ValueCountFrequency (%)
4 1
0.2%
4.032 1
0.2%
4.064 1
0.2%
4.096 1
0.2%
4.128 1
0.2%
4.16 1
0.2%
4.192 1
0.2%
4.224 1
0.2%
4.256 1
0.2%
4.288 1
0.2%
ValueCountFrequency (%)
19 1
0.2%
18.984 1
0.2%
18.968 1
0.2%
18.952 1
0.2%
18.936 1
0.2%
18.92 1
0.2%
18.904 1
0.2%
18.888 1
0.2%
18.872 1
0.2%
18.856 1
0.2%

bo
Real number (ℝ)

Distinct626
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.9
Minimum82
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:14.190895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile83.5096
Q188.5
median91
Q394.25
95-th percentile96.4968
Maximum97
Range15
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.902483
Coefficient of variation (CV)0.042931606
Kurtosis-0.66608277
Mean90.9
Median Absolute Deviation (MAD)2.804
Skewness-0.36065533
Sum57267
Variance15.229374
MonotonicityNot monotonic
2023-10-04T14:43:14.675217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 2
 
0.3%
88 2
 
0.3%
92 2
 
0.3%
90 2
 
0.3%
89.84 1
 
0.2%
94.616 1
 
0.2%
92.648 1
 
0.2%
85.936 1
 
0.2%
85.6 1
 
0.2%
96.136 1
 
0.2%
Other values (616) 616
97.8%
ValueCountFrequency (%)
82 1
0.2%
82.048 1
0.2%
82.096 1
0.2%
82.144 1
0.2%
82.192 1
0.2%
82.24 1
0.2%
82.288 1
0.2%
82.336 1
0.2%
82.384 1
0.2%
82.432 1
0.2%
ValueCountFrequency (%)
97 1
0.2%
96.984 1
0.2%
96.968 1
0.2%
96.952 1
0.2%
96.936 1
0.2%
96.92 1
0.2%
96.904 1
0.2%
96.888 1
0.2%
96.872 1
0.2%
96.856 1
0.2%

rem
Real number (ℝ)

Distinct626
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.5
Minimum60
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:15.143938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile65.032
Q181.25
median90
Q398.75
95-th percentile103.742
Maximum105
Range45
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation11.893747
Coefficient of variation (CV)0.13439262
Kurtosis-0.5962989
Mean88.5
Median Absolute Deviation (MAD)8.76
Skewness-0.57387866
Sum55755
Variance141.46121
MonotonicityNot monotonic
2023-10-04T14:43:15.581404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 2
 
0.3%
85 2
 
0.3%
100 2
 
0.3%
80 2
 
0.3%
74.08 1
 
0.2%
101.44 1
 
0.2%
95.6 1
 
0.2%
79.52 1
 
0.2%
81.08 1
 
0.2%
103.28 1
 
0.2%
Other values (616) 616
97.8%
ValueCountFrequency (%)
60 1
0.2%
60.16 1
0.2%
60.32 1
0.2%
60.48 1
0.2%
60.64 1
0.2%
60.8 1
0.2%
60.96 1
0.2%
61.12 1
0.2%
61.28 1
0.2%
61.44 1
0.2%
ValueCountFrequency (%)
105 1
0.2%
104.96 1
0.2%
104.92 1
0.2%
104.88 1
0.2%
104.84 1
0.2%
104.8 1
0.2%
104.76 1
0.2%
104.72 1
0.2%
104.68 1
0.2%
104.64 1
0.2%

sr.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct501
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7
Minimum0
Maximum9
Zeros127
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:16.018877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median3.5
Q36.5
95-th percentile8.4968
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.054572
Coefficient of variation (CV)0.82556
Kurtosis-1.4453469
Mean3.7
Median Absolute Deviation (MAD)3.004
Skewness0.17511259
Sum2331
Variance9.3304102
MonotonicityNot monotonic
2023-10-04T14:43:16.440720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127
 
20.2%
2 2
 
0.3%
5 2
 
0.3%
7 2
 
0.3%
0.752 1
 
0.2%
5.432 1
 
0.2%
4.568 1
 
0.2%
3.92 1
 
0.2%
0.544 1
 
0.2%
3.824 1
 
0.2%
Other values (491) 491
77.9%
ValueCountFrequency (%)
0 127
20.2%
0.016 1
 
0.2%
0.032 1
 
0.2%
0.048 1
 
0.2%
0.064 1
 
0.2%
0.08 1
 
0.2%
0.096 1
 
0.2%
0.112 1
 
0.2%
0.128 1
 
0.2%
0.144 1
 
0.2%
ValueCountFrequency (%)
9 1
0.2%
8.984 1
0.2%
8.968 1
0.2%
8.952 1
0.2%
8.936 1
0.2%
8.92 1
0.2%
8.904 1
0.2%
8.888 1
0.2%
8.872 1
0.2%
8.856 1
0.2%

hr
Real number (ℝ)

Distinct626
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.5
Minimum50
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-10-04T14:43:16.940689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51.258
Q156.25
median62.5
Q372.5
95-th percentile82.484
Maximum85
Range35
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation9.915277
Coefficient of variation (CV)0.15372523
Kurtosis-0.95725379
Mean64.5
Median Absolute Deviation (MAD)7.5
Skewness0.45586255
Sum40635
Variance98.312719
MonotonicityNot monotonic
2023-10-04T14:43:17.409409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 2
 
0.3%
60 2
 
0.3%
75 2
 
0.3%
55 2
 
0.3%
53.52 1
 
0.2%
77.88 1
 
0.2%
66.2 1
 
0.2%
54.88 1
 
0.2%
56.08 1
 
0.2%
81.56 1
 
0.2%
Other values (616) 616
97.8%
ValueCountFrequency (%)
50 1
0.2%
50.04 1
0.2%
50.08 1
0.2%
50.12 1
0.2%
50.16 1
0.2%
50.2 1
0.2%
50.24 1
0.2%
50.28 1
0.2%
50.32 1
0.2%
50.36 1
0.2%
ValueCountFrequency (%)
85 1
0.2%
84.92 1
0.2%
84.84 1
0.2%
84.76 1
0.2%
84.68 1
0.2%
84.6 1
0.2%
84.52 1
0.2%
84.44 1
0.2%
84.36 1
0.2%
84.28 1
0.2%

sl
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
3
126 
1
126 
0
126 
2
126 
4
126 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters630
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row3
5th row0

Common Values

ValueCountFrequency (%)
3 126
20.0%
1 126
20.0%
0 126
20.0%
2 126
20.0%
4 126
20.0%

Length

2023-10-04T14:43:17.878122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-04T14:43:18.381130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 126
20.0%
1 126
20.0%
0 126
20.0%
2 126
20.0%
4 126
20.0%

Most occurring characters

ValueCountFrequency (%)
3 126
20.0%
1 126
20.0%
0 126
20.0%
2 126
20.0%
4 126
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 630
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 126
20.0%
1 126
20.0%
0 126
20.0%
2 126
20.0%
4 126
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 126
20.0%
1 126
20.0%
0 126
20.0%
2 126
20.0%
4 126
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 126
20.0%
1 126
20.0%
0 126
20.0%
2 126
20.0%
4 126
20.0%

Interactions

2023-10-04T14:43:06.149504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:47.593132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:50.248622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:52.947065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:55.690009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:58.389631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:01.084388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:03.570251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:06.461036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:47.905420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:50.565717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:53.267854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:56.010099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:58.719681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:01.377343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:03.871118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:06.794368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:48.253972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:50.914922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:53.607191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:56.352520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:59.056388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:01.704350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:04.226653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:07.139429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:48.612164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:51.255542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:53.962566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:56.724715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:59.384042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:02.007300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:04.546829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:07.522387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:48.978590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:51.620092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:54.323056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:57.054917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:59.715466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:02.332211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:04.887085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:07.852398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:49.316400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:51.961500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:54.713349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:57.396421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:00.061882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:02.645948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:05.212401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:08.142305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:49.608814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:52.278811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:55.033133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:57.711159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:00.388576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:02.952867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:05.526325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:08.459733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:49.901797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:52.598710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:55.365211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:42:58.052389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:00.694032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:03.241999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-04T14:43:05.823792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-04T14:43:18.744258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
srrrtlmboremsr.1hrsl
sr1.0001.000-0.9201.000-0.9201.000-0.9311.0000.936
rr1.0001.000-0.9201.000-0.9201.000-0.9311.0000.884
t-0.920-0.9201.000-0.9201.000-0.9200.996-0.9200.904
lm1.0001.000-0.9201.000-0.9201.000-0.9311.0000.900
bo-0.920-0.9201.000-0.9201.000-0.9200.996-0.9200.876
rem1.0001.000-0.9201.000-0.9201.000-0.9311.0000.874
sr.1-0.931-0.9310.996-0.9310.996-0.9311.000-0.9310.844
hr1.0001.000-0.9201.000-0.9201.000-0.9311.0000.884
sl0.9360.8840.9040.9000.8760.8740.8440.8841.000

Missing values

2023-10-04T14:43:09.310043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-04T14:43:09.877521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

srrrtlmboremsr.1hrsl
093.8025.68091.84016.60089.84099.601.84074.203
191.6425.10491.55215.88089.55298.881.55272.763
260.0020.00096.00010.00095.00085.007.00060.001
385.7623.53690.76813.92088.76896.920.76868.843
448.1217.24897.8726.49696.24872.488.24853.120
556.8819.37695.3769.37694.06483.446.37658.441
647.0016.80097.2005.60095.80068.007.80052.000
750.0018.00099.0008.00097.00080.009.00055.000
845.2816.11296.1684.22495.11261.127.11250.280
955.5219.10495.1049.10493.65682.766.10457.761
srrrtlmboremsr.1hrsl
62097.02427.02486.28017.51283.536101.280.00077.564
62153.20018.64094.6408.64092.96081.605.64056.601
62265.44020.54492.54410.54490.54487.722.81661.362
62398.14428.14487.68018.07285.216102.680.00080.364
62458.80019.76095.7609.76094.64084.406.76059.401
62569.60020.96092.96010.96090.96089.803.44062.402
62648.44017.37698.0646.75296.37673.768.37653.440
62797.50427.50486.88017.75284.256101.880.00078.764
62858.64019.72895.7289.72894.59284.326.72859.321
62973.92021.39293.39211.39291.39291.964.08863.482